A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory 10K Stars ⭐️, a persistent memory engine for AI coding agents.

This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.

Currently, Working on AKBP: Agent Knowledge Base Protocol based on my findings, a protocol for creating, updating, retrieving, and sharing durable knowledge across AI agents.

使用JAV金鸡儿奖官网附带的工具JAV SQL 查询器，可查询各种类别的JavDB TOP250影片：

• JavDB TOP250
• JavDB 有码 TOP250
• JavDB 无码 TOP250
• JavDB 欧美 TOP250
• JavDB FC2 TOP250

及分年数据（存在部分重复影片，原始数据的问题）：

Export all your ChatGPT conversations as JSON + Markdown + HTML + ZIP. Works with ChatGPT Business/Team/Enterprise accounts (including SSO/Okta).

• JSON — Raw conversation data from the API
• Markdown — Clean text with headers per message, relative links to downloaded files
• HTML — ChatGPT-style conversation viewer with sidebar navigation, syntax-highlighted code blocks, and embedded images

Linear interpolation. Given a start, an end, and a fraction t ∈ [0,1], lerp returns a value in between:

lerp(a, b, t) = a + (b - a) * t

• t = 0 ➜ start
• t = 1 ➜ end

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.